Category: Using

Finding the next groundbreaking polymer is always a challenge, but now Georgia Tech researchers are using artificial intelligence (AI) to shape and transform the future of the field. Rampi Ramprasad’s group develops and adapts AI algorithms to accelerate materials discovery.

This summer, two papers published in the Nature family of journals highlight the significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first, featured in Nature Reviews Materials, showcases recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second, published in Nature Communications, focuses on the use of AI algorithms to discover a subclass of polymers for electrostatic energy storage, with the designed materials undergoing successful laboratory synthesis and testing.

“In the early days of AI in materials science, propelled by the White House’s Materials Genome Initiative over a decade ago, research in this field was largely curiosity-driven,” said Ramprasad, a professor in the School of Materials Science and Engineering. “Only in recent years have we begun to see tangible, real-world success stories in AI-driven accelerated polymer discovery. These successes are now inspiring significant transformations in the industrial materials R&D landscape. That’s what makes this review so significant and timely.”

AI Opportunities

Ramprasad’s team has developed groundbreaking algorithms that can instantly predict polymer properties and formulations before they are physically created. The process begins by defining application-specific target property or performance criteria. Machine learning (ML) models train on existing material-property data to predict these desired outcomes. Additionally, the team can generate new polymers, whose properties are forecasted with ML models. The top candidates that meet the target property criteria are then selected for real-world validation through laboratory synthesis and testing. The results from these new experiments are integrated with the original data, further refining the predictive models in a continuous, iterative process.

While AI can accelerate the discovery of new polymers, it also presents unique challenges. The accuracy of AI predictions depends on the availability of rich, diverse, extensive initial data sets, making quality data paramount. Additionally, designing algorithms capable of generating chemically realistic and synthesizable polymers is a complex task.

The real challenge begins after the algorithms make their predictions: proving that the designed materials can be made in the lab and function as expected and then demonstrating their scalability beyond the lab for real-world use. Ramprasad’s group designs these materials, while their fabrication, processing, and testing are carried out by collaborators at various institutions, including Georgia Tech. Professor Ryan Lively from the School of Chemical and Biomolecular Engineering frequently collaborates with Ramprasad’s group and is a co-author of the paper published in Nature Reviews Materials.

“In our day-to-day research, we extensively use the machine learning models Rampi’s team has developed,” Lively said. “These tools accelerate our work and allow us to rapidly explore new ideas. This embodies the promise of ML and AI because we can make model-guided decisions before we commit time and resources to explore the concepts in the laboratory.”

Using AI, Ramprasad’s team and their collaborators have made significant advancements in diverse fields, including energy storage, filtration technologies, additive manufacturing, and recyclable materials.

Polymer Progress

One notable success, described in the Nature Communications paper, involves the design of new polymers for capacitors, which store electrostatic energy. These devices are vital components in electric and hybrid vehicles, among other applications. Ramprasad’s group worked with researchers from the University of Connecticut.

Current capacitor polymers offer either high energy density or thermal stability, but not both. By leveraging AI tools, the researchers determined that insulating materials made from polynorbornene and polyimide polymers can simultaneously achieve high energy density and high thermal stability. The polymers can be further enhanced to function in demanding environments, such as aerospace applications, while maintaining environmental sustainability.

“The new class of polymers with high energy density and high thermal stability is one of the most concrete examples of how AI can guide materials discovery,” said Ramprasad. “It is also the result of years of multidisciplinary collaborative work with Greg Sotzing and Yang Cao at the University of Connecticut and sustained sponsorship by the Office of Naval Research.”

Industry Potential

The potential for real-world translation of AI-assisted materials development is underscored by industry participation in the Nature Reviews Materials article. Co-authors of this paper also include scientists from Toyota Research Institute and General Electric. To further accelerate the adoption of AI-driven materials development in industry, Ramprasad co-founded Matmerize Inc., a software startup company recently spun out of Georgia Tech. Their cloud-based polymer informatics software is already being used by companies across various sectors, including energy, electronics, consumer products, chemical processing, and sustainable materials.

“Matmerize has transformed our research into a robust, versatile, and industry-ready solution, enabling users to design materials virtually with enhanced efficiency and reduced cost,” Ramprasad said. “What began as a curiosity has gained significant momentum, and we are entering an exciting new era of materials by design.”

The study, recently published in the journal Molecular Phylogenetics and Evolution, suggests that the genetic variation of two species, the Brazilian sibilator frog and the granular toad, both amphibians native to northeastern Brazil, were shaped by different processes.

Results showed that the genetic variation in the sibilator frog was shaped mostly by population demographic events in response to habitat changes that occurred over the last 100,000 years. In contrast, genetic diversity in the granular toad was mostly shaped by contemporary landscape factors — toads that are relatively more isolated, either by geographic distance or inhospitable habitat, were more likely to be genetically different.

While previous investigations have explored the effects of historical demographic and landscape factors on genetic diversity of these amphibians, they were conducted with separate sets of data for these factors, making it difficult to discern which was the most important. Now, researchers involved with this paper are the first to use artificial intelligence to consider how both processes shape genetic diversity equally, rather than making manual assumptions about which may have been more vital.

“Prior to this work, we had to ask questions independently because you couldn’t investigate both influences in the same framework,” said Bryan Carstens, co-author of the study and a professor in evolution, ecology and organismal biology at The Ohio State University. “What AI allows us to do is to simulate processes that are both happening ecologically in the present and during deep-time evolutionary events and compare those findings to the actual data that we collect from these frogs.”

Due to the sheer amount of data that’s become available to geneticists and other wildlife biologists over the past few decades, it can be challenging for researchers to identify specific factors that might be important in certain experiments, said Carstens. But by integrating large swaths of information into simulations that can account for those elements in a single analysis, it’s possible to get a much more complete chronicle of a species’ development.

“It takes a long time to build and train our AI models, but we wanted one l that could capture the range of potential variation in the species’ histories in a way that was as faithful as we could be to what we knew about the biology of the system,” said Carstens.

For example, while the species this study investigated dwell in the same region, there are many differences in their natural histories. Despite both their eggs and larvae being fully aquatic, the sibulator frog reproduces continuously throughout the wet season and in underground chambers, while the granular toad’s reproductive events happen explosively because they are dependent on heavy rainfall.

Combined with their machine learning approach, the researchers’ simulation determined their model scenarios were 100% supported regarding historical explanations for the sibilator frog’s expansion, and over 99% supported for those of the granular toad.

One of the reasons their model is so accurate is due to its ability to account for recent demographic events, including measuring how events like human development or habitat change may have affected animal genetic diversity over a long period of time.

But even when using AI, researchers have to be careful to avoid deceptive patterns in their results, said Carstens.

“No analysis that we do is going to capture every single factor that has been important to these species over millions of years,” he said. “So we have to allow for a range of possibilities without making it so broad that essentially any model would be able to fit the data.”

That said, as technological strides allow researchers to answer niche ecological questions and test new hypotheses, their work is a precursor to creating an upgraded machine learning framework that could be applied to unique investigations of other species, said Carstens.

“We’re likely to continue using different combinations of these AI tools in different ways to try to understand evolutionary history,” said Carstens. “And as we keep learning, the tools we’re using will change, and they’ll evolve to be even better.”

Emanuel M. Fonseca, who earned his doctorate from Ohio State in 2022, was a co-author. The study was supported by the Ohio Supercomputer Center, the U.S. National Science Foundation and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior in Brazil.